Soil moisture-evaporation coupling shifts into new gears under increasing CO2

When soil moisture (SM) content falls within a transitional regime between dry and wet conditions, it controls evaporation, affecting atmospheric heat and humidity. Accordingly, different SM regimes correspond to different gears of land-atmosphere coupling, affecting climate. Determining patterns of SM regimes and their future evolution is imperative. Here, we examine global SM regime distributions from ten climate models. Under increasing CO2, the range of SM extends into unprecedented coupling regimes in many locations. Solely wet regime areas decline globally by 15.9%, while transitional regimes emerge in currently humid areas of the tropics and high latitudes. Many semiarid regions spend more days in the transitional regime and fewer in the dry regime. These imply that a larger fraction of the world will evolve to experience multiple gears of land-atmosphere coupling, with the strongly coupled transitional regime expanding the most. This could amplify future climate sensitivity to land-atmosphere feedbacks and land management.

I do not wish to remain anonymous -Andrew Feldman. . Based on those studies, one could argue that we are still unsure how well these climate models are really capturing land-atmosphere interactions given their large variance in the EF-soil moisture relationships. EF-soil moisture relationships are emergent behavior from many parameterizations (for example, plant-soil-climate interactions defining WP and CSM which are still highly uncertain in these global models), each having their own errors and differences between models. Additionally, precipitation projections are yet uncertain with heterogeneous changes that translate to uncertain changes in soil moisture trends globally as well (see studies by Alexis Berg). Ultimately, Figure 4 aggregates all of these uncertainties into how much more time is being spent in a water-limited evaporative stage in a changing climate.

Major Comments 1) A major unanswered question and lynchpin of this study is how well CMIP
Unless the authors argue differently that there have been many previous studies supporting how well climate models capture land surface evaporative regimes and soil moisture trends, I highly recommend that the authors devote more text to addressing these issues as a main shortcoming of the study that it is still not well known how well CMIP/climate models capture land-atmosphere coupling and regimes. Personally, I think we need more explicit observational studies that can be used to assess how well climate models are capturing water-limited evaporative regimes (Both the shapes of EFsoil moisture relationships and time spent in the water-limited regime as two example metrics). There are unfortunately not many observational, model-free options that can be used to test how well the ensemble model classifications in Figure 2  2) I found the motivation to be lacking a bit here. The authors could do a lot more to explain why we should care that land surface processes are tending to become more water-limited.
Line Specific Comments L55-57: Being rigorous, L55 "will not cause greater evaporation" and L57 "LE is also insensitive to SM variations" (and in caption in L625) are too definite of statements and may not be entirely correct. Soil moisture increases can still cause small evaporation increases in non-water limited regimes, but EF variations depend mainly on other factors. Not required to reference but: Feldman et al. 2022 Fig. 6B+6C show with observations that there is still some energy flux sensitivity to soil moisture in the "wet regime" but it is just less. L75: Given my major comment, I think more motivation should be given for why the CMIP6 models are chosen. I am not a modeler so I'll word as a question: could we trust the analysis more if done with more land surface-focused models (CESM, CATCHMENT-CN) rather than ESM's that are coupled to a highly parametrized atmosphere and may have more complex land surface schemes (i.e., plant hydraulics) turned off?  : This binary metric of migration is helpful. However, it would be more helpful to also know how much more time is being spent in the transitional regime as well, which is (I think) largely dictated by trends in soil moisture in the models. A figure that shows a percentage of time change would add more meaningful information on top of that of the more binary metric in figure 4. L315: As a confidence test, the authors could repeat the analysis with AIC. BIC here may tend to select less parameterized models (those with less soil moisture breakpoints) while AIC may find more parameterized models. It is difficult to assess if this would change the main results in figure 4. This is optional given that there is no basis for arguing that AIC is better/worse than BIC. It just provides another sensitivity test for Figure 4 and how much regime classifications matter for the final results.

Reviewer #2 (Remarks to the Author):
Hsu et al., utilize the latest model simulations to identify possible changes in soil moisture control on latent heat flux globally under increasing CO2 concentrations. Overall, this paper is well-written and methodologically sound. The authors classify 5 main regimes, but I think adding C100 is important, which has different underpinning drivers compared to C001. In addition, current analyses are only on "how soil moisture regimes change?". I think it is very critical to do more analyses to show "why soil moisture regimes change?" and "Why models differ a lot?" Only discussions are not sufficient.

Other Comments:
Abstract: Current version is too general. It is good to complement some region hotspots and adequate numbers.

Reviewer #1 (Remarks to the Author):
Hsu and Dirmeyer classify evaporative regimes using several climate models and determine that land surfaces are on average projected to be in more water-limited evaporative states. I find this to be an important and timely study. It is methodologically sound in using previously developed methods to identify evaporative regimes and having proper confidence tests -I would have done all the same steps myself and the authors answered all of my methodology questions in their text. I support the study, but have some doubts about how well ESMs at the CMIP level, which tend to have very simplistic representations of the land surface, can really capture EF-soil moisture relationships and soil moisture trends that drive the main results. See my major comment for more details. I acknowledge it is difficult for the authors to address this concern, but I recommend some ways they may be able to provide some confidence in the model results.

I do not wish to remain anonymous -Andrew Feldman.
We thank Dr. Feldman for the comments. Mainly, we have strengthened our motivation and more clearly set the frame of this study. As most of the additional analysis requested has already been provided either in our previous study or in the supplementary information, most of these comments are addressed by modification of the manuscript to make those connections clearly. We have added more description to the text discussing SM regime bias in our previous work, in which a comparison between models and observationally-based data had been made, and we provide a clearer linkage to the supplementary information, in which the spatial pattern of the time soil moisture spends in different regimes has been provided. We also checked results by using AIC and confirm that using AIC versus BIC for model selection only negligibly changes our results. Based on those studies, one could argue that we are still unsure how well these climate models are really capturing land-atmosphere interactions given their large variance in the EF-soil moisture relationships. EF-soil moisture relationships are emergent behavior from many parameterizations (for example, plant-soil-climate interactions defining WP and CSM which are still highly uncertain in these global models), each having their own errors and differences between models. Additionally, precipitation projections are yet uncertain with heterogeneous changes that translate to uncertain changes in soil moisture trends globally as well (see studies by Alexis Berg). Ultimately, Figure 4 aggregates all of these uncertainties into how much more time is being spent in a water-limited evaporative stage in a changing climate.

Major Comments 1) A major unanswered question and lynchpin of this study is how well
Unless the authors argue differently that there have been many previous studies supporting how well climate models capture land surface evaporative regimes and soil moisture trends, I highly recommend that the authors devote more text to addressing these issues as a main shortcoming of the study that it is still not well known how well CMIP/climate models capture land-atmosphere coupling and regimes. Personally, I think we need more explicit observational studies that can be used to assess how well climate models are capturing water-limited evaporative regimes (Both the shapes of EF-soil moisture relationships and time spent in the water-limited regime as two example metrics). There are unfortunately not many observational, model-free options that can be used to test how well the ensemble model classifications in Figure 2  Thank you for the detailed comment. We agree that it is needed to valid models' SM regimes with observations. As the reviewer has mentioned, pre-industrial simulations instead of historical simulations are used here, and thus comparing to the current climate is impractical -the models' historical simulations are appropriate for such a comparison. Nevertheless, we did compare the SM regime in historical simulations to reanalysis and SMAP L4 products in our previous study (Hsu and Dirmeyer 2022). The difference was discussed in our original main text (Line 277-291). At this stage, reanalyses and SMAP L4 are the only products that meet global validation requirements and analyses have already been provided in the published literature. As a purely in situ set of observations with a long enough time series of daily SM and LE data (covering a few decades) is not available, we are not able to provide additional analysis here (as the reviewer has also mentioned). We admit that the description in the discussion section of original manuscript was not sufficient. To emphasize this issue more, we have added more description as in Line 291-297: "Regarding these issues, a stricter validation of models' SM regimes may become possible in the future, based on promising developments in global SM observations (e.g., Rodriguez-Fernandez et al. 2021;O and Orth 2021;Beck et al. 2021;Chaney et al. 2019;Seo and Dirmeyer 2022). A more comprehensive analysis of each component of SM-induced feedback in climate models will further help to evaluate the credibility of this study and to understand the causes of bias and change of models' SM regimes." 2) I found the motivation to be lacking a bit here. The authors could do a lot more to explain why we should care that land surface processes are tending to become more water-limited. Thank you for the comment. We admit that the motivation was not clearly made in our original main text. We have integrated the Line Specific Comments L71 to addressing this comment. In the revision, we have added more description and cite more papers to clarify our scientific question, and to narrow down to our motivation as the following: . However, climate in many locations is composed of days with and without active SM:LE coupling and thus the existence of SM-induced feedbacks. This suggests that under global warming, in addition to a stronger control of SM on LE, the climatological strengthening in coupling can also be attributed to a more frequent control of SM on LE, i.e. more days when SM is between WP and CSM, or even the situation where a transitional SM regime emerges locally when it did not exist previously. Thus, investigation on whether a warming climate leads to the shift, emergence, or disappearance of SM regimes, along with corresponding changes in the frequency of SM in each SM regime, has been lacking but is needed.
In this study, we determine the global patterns of existing SM regimes and their projected changes from state-of-the-art climate models. This enables quantification of how SM values migrate among dry, transitional, and wet regimes due to global warming. Such diagnostic analyses can indicate which gear of SM-induced feedback is dominant at any location, and what changes may occur." (Line 75-99) Line Specific Comments L55-57: Being rigorous, L55 "will not cause greater evaporation" and L57 "LE is also insensitive to SM variations" (and in caption in L625) are too definite of statements and may not be entirely correct. Soil moisture increases can still cause small evaporation increases in non-water limited regimes, but EF variations depend mainly on other factors. Not required to reference but: Feldman et al. 2022 Fig. 6B+6C show with observations that there is still some energy flux sensitivity to soil moisture in the "wet regime" but it is just less. Thank you for pointing this out. We agree that the statement is not completely accurate. This is modified in the revised manuscript as "When SM>CSM, SM does not regulate evaporation (Dirmeyer et al. 2020;Feldman et al. 2022). Evaporation may even decline, as very wet soils correspond to periods of rainfall, cloudiness and limited sunshine." (Line 56-58).

L71: A stronger, more explicit research question or objective is needed here. Stronger motivation is needed on why we should know the answer to this question and how well others have answered this previously. For example, Dirmeyer et al. 2012 referenced here have previously shown a projected increase in land atmosphere coupling strength with climate change which may or may not relate to the investigation here.
Thank you for the comment. We should have pointed out that an increase in coupling strength does not ensure a more moisture-limited world. Thus, examining the SM regime helps to address this issue. We admit that this argument was not clear in our original main text. While integrating the major comment #2 on motivation, this has been modified as the following: . However, climate in many locations is composed of days with and without active SM:LE coupling and thus the existence of SM-induced feedbacks. This suggests that under global warming, in addition to a stronger control of SM on LE, the climatological strengthening in coupling can also be attributed to a more frequent control of SM on LE, i.e. more days when SM is between WP and CSM, or even the situation where a transitional SM regime emerges locally when it did not exist previously. Thus, investigation on whether a warming climate leads to the shift, emergence, or disappearance of SM regimes, along with corresponding changes in the frequency of SM in each SM regime, has been lacking but is needed.
In this study, we determine the global patterns of existing SM regimes and their projected changes from state-of-the-art climate models. This enables quantification of how SM values migrate among dry, transitional, and wet regimes due to global warming. Such diagnostic analyses can indicate which gear of SM-induced feedback is dominant at any location, and what changes may occur." (Line 75-99)

L75: Given my major comment, I think more motivation should be given for why the CMIP6 models are chosen. I am not a modeler so I'll word as a question: could we trust the analysis more if done with more land surface-focused models (CESM, CATCHMENT-CN) rather than ESM's that are coupled to a highly parametrized atmosphere and may have more complex land surface schemes (i.e., plant hydraulics) turned off?
Thank you for the question. We agree that analysis done with more complex land models would provide more insight. However, since our goal here is to obtain a model consensus result, model outputs from available CMIP6 data is the best choice, since their simulations have a consistent configuration as well as good (but not universal) availability of SM and LE data at daily time scale covering several decades. About the credibility between using CMIP6 and land surface-focused models, we are not able to provide a certain answer. Although how detailed a model represents physical processes does contribute to the credibility, a multi-model result can also enhance credibility from a statistical perspective. We believe opting for data availability to obtain a consensus result is the best choice for our study. To point out the motivation of selecting multiple CMIP6 models, we briefly describe the reason in the introduction.
"To examine these responses under warming with a climate model consensus perspective, daily data from eight climate models..." (Line 97) Thank you for the comment. First, we have to correct the mistake that it should be 3.6% instead of 2.6% (Line 133 in modified main text). Then, we would like to clarify that this 3.6% means the areas with an emerging transitional regime. For areas with changing regime dominance, it is 13.3% by summing the grid cell areas marked with a star (where at least one regime appears or disappears). We provide this number in Line 116 "...this accounts for 13.3% of the global analyzed area." in the modified manuscript.
Finally, we would like to argue that 3.6% is not a small number since only 23.3% of the global area has no transitional regime in the pre-industrial climate, and thus 3.6/23.3%=15% increase in area where SM:LE becomes coupled under global warming. This argument is added in the revised manuscript: "Only 23.3% of global land areas are identified as uncoupled (C001) for pre-industrial climate; a 3.6% expansion in global area with active SM:LE amounts to a shrinkage of 15% in uncoupled area." (Line 135-137) We agree that examining the changing time in the water-limited evaporative stage is informative. Actually, this has been provided in the supplementary information ( Fig S3). However, as the average of these patterns among climate models has large spread, due to inconsistent detection of WP and/or CSM among models, we only provide individual model results and not a consensus. As we prefer to focus on composite results in the main text, we chose to put these results in the supplementary information. In the modified text, we enhance the linkage between the main text and this individual result: "Individual model changes are displayed in Fig.S3. Spread among climate models at any location can be large. This is mainly due to differences among models in the detection of WP and/or CSM. Nevertheless, the trend of how SM migrates in a changing climate demonstrates strong agreement in many regions." (Line 1714-178) Thank you for the comment. We have added more description for interpreting Figure 4 in line 187-189: "…and migration from dry to transitional (cyan) is the major tendency across the globe (42.3%)".
We also want to point out that Figure 2 instead of Figure 4 contains our main message as Figure 2 shows an expansion of certain SM regimes under warming, which inspires the title "coupling shifts into new gears". Moreover, as both "the existence of a SM regime" and "the time SM spends in each regime" under warming are both rather unexplored topics. The former topic seems to be more large-frame, we believe Figure 4 plays a complementary role to Figure 2. To make this clearer, we additionally hint at the hypernym hierarchy of the results in our objective in the introduction: "...Thus, investigation on whether a warming climate leads to the shift, emergence, or disappearance of SM regimes, along with corresponding changes in the frequency of SM in each SM regime, has been lacking but is needed.
In this study, we determine the global patterns of existing SM regimes and their projected changes from state-of-the-art climate models. This enables quantification of how SM values migrate among dry, transitional, and wet regimes due to global warming." (Line 88-97) Thank you for the comment. We agree that the migrating of SM among regimes can be attributed to the trend (also the expansion or narrowing of the distribution). We do quantify how the percentage of time in each SM regime changes, as shown by FigS3. As mentioned above (reviewer's comment on Figure  2), we now link more clearly to this supplementary information in our main text and provide more discussion.
L315: As a confidence test, the authors could repeat the analysis with AIC. BIC here may tend to select less parameterized models (those with less soil moisture breakpoints) while AIC may find more parameterized models. It is difficult to assess if this would change the main results in figure 4. This is optional given that there is no basis for arguing that AIC is better/worse than BIC. It just provides another sensitivity test for Figure 4 and how much regime classifications matter for the final results.
Thank you for the comment. We have repeated the analysis selecting the models using AIC. The results are shown below. Comparing to the original result (Fig S2), we see only sporadic differences at a few locations with the different classification technique. For example, a few more green grid cells are found over the South Great Plains by using AIC. Overall, this indicates that penalty terms accounting for the number of parameters either in AIC or BIC does not substantially affect the regression selection. The extra check suggested by the reviewer solidifies our confidence in the test. We now state at Line 373: "Akaike information criterion was also tested and found to produce nearly identical results."

Reviewer #2 (Remarks to the Author):
Hsu et al., utilize the latest model simulations to identify possible changes in soil moisture control on latent heat flux globally under increasing CO2 concentrations. Overall, this paper is well-written and methodologically sound. The authors classify 5 main regimes, but I think adding C100 is important, which has different underpinning drivers compared to C001. In addition, current analyses are only on "how soil moisture regimes change?". I think it is very critical to do more analyses to show "why soil moisture regimes change?" and "Why models differ a lot?" Only discussions are not sufficient.
Thank you for the comment. C100 could provide a different perspective for determining SM regimes, and we initially sought to include it in our analysis. However, we would like to argue that defining C100 for this study is not practical due to the following reasons: (1) In the previous study using the same SM regime detection method, a location was defined as C100 where the standard deviation of SM remains very close to zero regardless which regression best fits the SM-LE data. This was meant to classify the persistently dry desert areas as C100. However, for this study where we seek to determine the fraction of SM states in each SM regime, the definition is at odds with the notion of variable soil moisture. In reality and models in arid regions, daily SM can still exceed the WP or even CSM since sporadic rainfall events happen within a 50 year period, which trigger LE very effectively. Such a SM-LE coupling has also been evidenced in observations (e.g., Agam (Ninari) et al., 2004). Thus, we think forcing desert grid cells to be C100 is not practical or realistic. (2) C100 and C001 both indicate that a flat line best fits the SM-LE data. This means it requires an additional step to separate them, e.g., the standard deviation criterion described above to identify desert points. However, in our previous results, local SM-LE data that is best fitted by a flat line only occurred in rainforest, high latitude or high altitude areas -all energy-limited regimes. Thus setting an arbitrary criterion for separating C100 and C001 is ultimately unnecessary as all flat-line grid cells appear to be C001. This may have to do with model resolution -known areas that might fit C100 (e.g., the Atacama Desert) are not resolved by these climate models.
Determining the cause of differences among models is always a very difficult task. Every model inter-comparison project states this aim, and few accomplish much. The first step is to quantify the differences, which we do for these models and metrics. As this study already aims to diagnose SM regimes and their changes, examining the possible causes of the changes and the cause of differences among models is the next step, and beyond the scope of this paper. We think the results here are already adequate since they cover a rarely-explored topic for climate change; we hope this work can trigger discussion and further investigation. In the modified manuscript, we have added more description to set the scope of this study and to provide more discussion potential topics of the following work: "...Thus, investigation on whether a warming climate leads to the shift, emergence, or disappearance of SM regimes, along with corresponding changes in the frequency of SM in each SM regime, has been lacking but is needed.
In this study, we determine the global patterns of existing SM regimes and their projected changes from state-of-the-art climate models. This enables quantification of how SM values migrate among dry, transitional, and wet regimes due to global warming. Such diagnostic analyses can indicate which gear of SM-induced feedback is dominant at any location, and what changes may occur...." (Line 88-97) "Regarding these issues, a stricter validation of models' SM regimes may become possible in the future, based on promising developments in global SM observations (e.g., Rodriguez-Fernandez et al. 2021;O and Orth 2021;Beck et al. 2021;Chaney et al. 2019;Seo and Dirmeyer 2022). A more comprehensive analysis of each component of SM-induced feedback in climate models will further help to evaluate the credibility of this study and to understand the causes of bias and change of models' SM regimes." (Line 291-297)

Other Comments:
Abstract: Current version is too general. It is good to complement some region hotspots and adequate numbers.
We have attempted to accommodate the reviewer's suggestion, but the abstract length is very restrictive: 150 words. We are already at that limit. We now quote the 15% reduction in wet regime (C001) and highlight the expansion of the transitional regime more clearly, including potential consequences. There is not space to be explicit about specific regional hotspots. The possible consequences are now also stated in the discussion section (Line 208-212): "Furthermore, the expanding transitional regime could amplify local climate sensitivity to land-atmosphere feedbacks. This could make some regions more susceptible to unintended consequences from land management practices that alter soil moisture, such as irrigation, cropping choices, urbanization and water resource management." Figure 2 and 4: It is really hard to identify squares or stars. Please correct this.
Thank you for the comment. To make the plots clearer, we have modified the plot with more visible symbols and added to the caption.
For Figure 2, we have modified the legend symbols representing SM regimes under warming climate. Now, it is clearer that the star is always over the square (this also indicates that colored squares are everywhere). We also reinforce this point in the caption in Line 753 : "star color (over the square color) indicates a new candidate emerging in a warming climate at that grid cell." For Figure 4, to clearly separate the grid cells with agreement > 30% and >60%, the symbol for agreement > 60% has been changed from a star to a large multiplication sign.
These modified plots are shown below:  Lines 286-323: The Method is critical. Only citing references is not easy for readers. Please explicitly explain it.
Thank you for the suggestion. Actually, the description we have provided in our original Methods Summary is a step-to-step workflow for attaining our results. We have also explained the motivation behind each step. The schematic plots displaying all the SM regime candidates have been provided in Figure 2. Only some description details of the mathematics is excluded in this manuscript. Since we were not clear about the best approach, we asked the Associate Editor, Dr. Efi Rousi, and she said the original Methods Summary was adequate. Replicating all those descriptions would essentially duplicate the content of our previously published paper (Hsu and Dirmeyer 2022; https://doi.org/10.1175/JHM-D-21-0224.1), potentially raising issues of self-plagiarism. As we have clearly cited this paper, we choose to preserve the original description in the Methods Summary.